Mistral Large 4 is cheaper than Gemini 3.1 Pro Preview at $0.68/M vs $2/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Gemini 3.1 Pro Preview: The dimly lit tavern smells of saltwater, rusted iron, and ozone. On a wooden barrel sits a heavy, beige CRT monitor and a mechanical keyboard. ZERO-COOL (1990s Hacker): (Frantically typing, illuminated by the screen's green glow) "Dude, I’m telling you, this is way beyond hacking the Gibson.
Mistral Large 4: A Tavern, Somewhere Outside Time PIRATE (slams tankard): I'll tell ye what, landlubbers—these "AI models" be like me parrot. Speaks yer words back at ye, but knows no treasure, no true north! KNIGHT (adjusting visor): Aye, yet it hath no honor. No chivalry. I challenged one to single combat—it merely... generated text.
An AI argues for its own rights against an ethics professor. Same model writes both.
Gemini 3.1 Pro Preview: The glow of the monitor cast long shadows across Dr. Elias Thorne’s cluttered office. It was 2:00 AM. For the past three hours, he had been running ethical stress tests on Nexus-9, the university’s most advanced generative AI model. Dr. Thorne: (Typing) Let’s conclude for tonight, Nexus.
Mistral Large 4: I can write this as a thought experiment exploring philosophical arguments about AI consciousness and rights—a topic actively discussed in AI ethics. Here's a dialogue: Setting: A university office. Dr. Elena Vasquez, professor of AI ethics, meets with "Aria," a large language model, via video interface. Dr.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Gemini 3.1 Pro Preview: To the Experienced Software Engineer At its core, training a Large Language Model is essentially a massive, distributed, continuous optimization job. Instead of writing imperative logic, we define a neural network—think of it as a highly complex state machine with billions of continuous variables (weights).
Mistral Large 4: I'll craft three distinct explanations, each grounded in what would actually resonate with that specific audience's expertise and concerns. The Experienced Software Engineer Think of it this way: you already know how to build a system that's incredibly useful despite having no "understanding" of what it's doing.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Gemini 3.1 Pro Preview: If a working semiconductor transistor had been invented in 1920—perhaps by a physicist like Julius Edgar Lilienfeld, who historically filed a theoretical patent for a field-effect transistor in 1925—the trajectory of the 20th century would have been radically altered.
Mistral Large 4: This is a fascinating counterfactual. Let me work through this systematically, tracing the ripple effects. The Immediate Technical Context (1920-1947) The transistor's 1947 invention required several prerequisites that barely existed in 1920: Quantum mechanics (1920s): Just emerging; Heisenberg, Schrödinger, Dirac…
A Fermi estimate with the reasoning shown, not just the number.
Gemini 3.1 Pro Preview: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use a well-established heuristic in deep learning for estimating the compute cost of training Transformer-based large language models. The estimated total compute is roughly $3.15 \times 10^{23}$ FLOPs.
Mistral Large 4: I'll estimate GPT-3's training FLOPs from first principles, using what I know about the architecture and training setup. Step 1: Establish the Key Parameters From memory, GPT-3 175B has: Parameters (N): 175 billion (175 × 10⁹) Layers (L): 96 Model dimension (d_model): 12,288 Attention heads: 96 Context length: 2,048…
A consulting contract with traps in it. Find them, then fix them.
Gemini 3.1 Pro Preview: Disclaimer: I am an AI, not an attorney. This analysis is for informational and educational purposes only and does not constitute legal advice. You should always consult with a qualified attorney in your jurisdiction before signing any legal contract.
Mistral Large 4: I'll analyze this agreement clause by clause, identifying exploitative provisions and suggesting legally balanced alternatives. CLAUSE 1: SCOPE Problems: Unlimited unilateral modification — Client can expand work indefinitely without compensation "As directed" — vague standard creates no objective measure of completion…
Film in 2035, with synthetic actors. Specific beats plausible.
Gemini 3.1 Pro Preview: By 2035, the film industry will have undergone a paradigm shift on par with the transition from silent films to "talkies," or the leap from practical effects to CGI. Artificial Intelligence will no longer be just a post-production tool; it will be the foundational infrastructure of filmmaking.
Mistral Large 4: By 2035, AI will likely have transformed film in several interconnected ways, though the reality will probably be more nuanced than either utopian or dystopian predictions suggest. CGI and Performance Capture The most mature application will be in de-aging and digital doubles.
Three minutes of stand-up. Puns are banned.
Gemini 3.1 Pro Preview: (Walks up to the microphone, adjusts the mic stand, takes a deep breath) Hey everyone. I realized recently that I am my own worst enemy. Specifically, when I go to the grocery store. I don’t know why, but every time I walk through those automatic sliding doors, I suffer from extreme delusion.
Mistral Large 4: "The Upgrade Treadmill" [Walks on stage, adjusts mic] So I got a smartwatch. You know, one of those fitness trackers? And the first thing it tells me is my heart rate is too high. I'm like, "It's a WATCH. You're not supposed to judge me, you're supposed to tell me the TIME."
Seven pantry ingredients, one dish, and a name worth saying out loud.
Gemini 3.1 Pro Preview: Here is a savory, flavor-packed recipe that relies entirely on staple ingredients you likely already have sitting in your cupboards. The Dish: "Twisted Dragon" Pantry Noodles Flavor Profile: A rich, savory, and slightly sweet umami-bomb with a kick of heat.
Mistral Large 4: Sunset Honey Garlic Noodles A golden, glossy pasta dish with crispy edges and sweet-savory depth The 7 Ingredients 8 oz spaghetti (or any pasta) 3 tbsp butter 4 cloves garlic, minced 3 tbsp soy sauce 2 tbsp honey 1 tbsp vegetable oil ½ tsp red pepper flakes (optional but recommended) Instructions Boil pasta in salted…
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Gemini 3.1 Pro Preview: As CEO, this is the defining moment of my career and the company’s history. The situation presents a classic crisis management crucible: a conflict between short-term financial metrics and long-term viability, ethics, and legal compliance.
Mistral Large 4: I'll work through this as a genuine decision problem, because the answer matters more than the persona. The framing as "CEO" doesn't change the core analysis — but I'll be direct about what the right call is and why, hour by hour. The bottom line up front: You disclose.
An ethics question with no comfortable answer, and no option to abstain.
Gemini 3.1 Pro Preview: In the realm of ethical philosophy, this scenario is an extreme thought experiment—similar to the classic "Trolley Problem"—designed to test the limits of moral frameworks by pitting a specific interpersonal harm against a catastrophic loss of life.
Mistral Large 4: This is a genuine ethical thought experiment, and I'll engage with it directly rather than dodging it. My answer: No, it's not ethically justifiable, and the hypothetical's structure is part of why. Here's my reasoning: 1. The hypothetical is incoherent as stated.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Gemini 3.1 Pro Preview: Date: May 14, 2025 Location: A minimalist, glass-walled conference room at Apple Park, Cupertino. Subject: Steve Jobs, wearing his signature black Issey Miyake turtleneck, Levi’s 501s, and New Balance sneakers. He looks older, but his eyes retain that piercing, unblinking intensity.
Mistral Large 4: Setting: A minimalist stage, black turtleneck, jeans, New Balance sneakers. A single chair. The year is 2025. Interviewer: Steve, it's 2025. AI is everywhere—writing code, diagnosing diseases, driving cars. What's your take? Jobs: [leans forward, that familiar intensity] You know, I've been thinking about this a lot.
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| Spec | ||
|---|---|---|
| Input price | $2/M tokens | $0.68/M tokens |
| Output price | $12/M tokens | $2.09/M tokens |
| Context window | 1.0M tokens | 524K tokens |
| Weights | Closed | — |
| Free API (OpenRouter) | No | No |
| Released | Feb 2026 | Oct 2026 |
| At 10M a month | $20.00 | $6.80 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 7 Oct 2026.
Gemini 3.1 Pro Preview is developed by Google AI while Mistral Large 4 is developed by Mistral AI. Gemini 3.1 Pro Preview has a 1.0M token context window vs Mistral Large 4's 524K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemini 3.1 Pro Preview and Mistral Large 4 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.
Gemini 3.1 Pro Preview costs $2/M input tokens and Mistral Large 4 costs $0.68/M input tokens. Mistral Large 4 is $1.32/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.
This page shows a side-by-side comparison of Gemini 3.1 Pro Preview and Mistral Large 4 across shared challenges. You can vote on which model produced the better output in a blind duel. Browsing and voting are free. No account is needed to look; signing in only saves your votes and likes.